Virtual patient trials of a multi-input stochastic model for tight glycaemic control using insulin sensitivity and blood glucose data. (May 2020)

• Record Type: Journal Article
• Authors: Davidson, Shaun M.; Uyttendaele, Vincent; Pretty, Christopher G.; Knopp, Jennifer L.; Desaive, Thomas; Chase, J. Geoffrey
• Is Part Of: Biomedical signal processing and control. Volume 59(2020)
• Publication Date: 2020-05
• Subjects: Glycaemic control -- Stochastic model -- Gaussian Kernel -- Insulin sensitivity -- Stochastic targeted -- Virtual trials; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101896
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Performance of a multi-input stochastic model to predict insulin sensitivity is evaluated. Evaluation involves virtual trials of 1477 retrospective patients from multiple hospitals. The model decreased hyperglycaemic hours from 12.3 % using existing methods to 11.2 %. The model increased patient nutrition for a negligible increase in computation or work load. Overall, the model could provide greater personalisation and clinical performance. Abstract: Objective: Safe, effective glycaemic control (GC) requires accurate prediction of future patient insulin sensitivity ( SI ), balancing the risk of hyper- and hypo-glycaemia. The stochastic targeted (STAR) protocol combines a clinically validated metabolic model and SI metric with a risk-based stochastic approach to optimise patient specific insulin and feed rates. Validated virtual trials comparing a novel 3D stochastic model for prediction of future patient SI using current patient SI and current blood glucose ( BG ) to an existing 2D stochastic model for SI prediction were conducted. Methods: The virtual trials involved 1477 retrospective patients across two hospitals and two GC protocols. They were conducted using five-fold cross-validation to build each stochastic model, ensuring independent test data. Results: The 3D stochastic model shifted BG from the 4.4–8.0 mmol/L target band towards the lower 4.4–6.5 mmol/L band, providing a decrease from 12.31 % to 11.19 % in hyperglycaemic hours ( BG > 8.0 mmol/L), but only a 0.24 % increase, from 1.01 % to 1.25 %, in light hypoglycaemic hours ( BG < 4.0 mmol/L). Simultaneously, the 3D stochastic model enabled greater patient nutrition, and required negligible increase in computational or clinical workload. Conclusions: The 3D stochastic model provided greater personalisation and better realised STAR's design philosophy of minimising hyperglycaemic events for an acceptable clinical risk of 5.0 % BG < 4.4 mmol/L. Thus, this model could provide better clinical conformity to design targets if implemented within the STAR protocol.